Methods and systems for using multiple data structures to process surgical data

ABSTRACT

The present disclosure relates to processing data streams from a surgical procedure using multiple interconnected data structures to generate and/or continuously update an electronic output. Each surgical data structure is used to determine a current node associated with a characteristic of a surgical procedure and present relevant metadata associated with the surgical procedure. Each surgical data structure includes at least one node interconnected to one or more nodes of another data structure. The interconnected nodes between one or more data structures includes relational metadata associated with the surgical procedure.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims the benefit of and priority to U.S. ProvisionalApplication No. 62/639,348, filed Mar. 6, 2018, which is herebyincorporated by reference in its entirety for all purposes.

FIELD

The present disclosure generally relates to methods and systems forprocessing surgical data using multiple data structures for generatingand/or updating an electronic output. More specifically, the presentdisclosure relates to using multiple interconnected data structures toprocess surgical data from one or more data streams of a surgicalprocedure that is represented by interconnected surgical maps thatinclude precise and accurate descriptions of a surgical procedure.Additionally, the present disclosure relates to using amulti-dimensional artificial intelligence protocol to process surgicaldata from one or more data streams, using the multiple interconnecteddata structures, to identify a state in surgery, such that one or moredetected characteristics can be compared to one or more targetcharacteristics corresponding to the state of surgery to generate and/orupdate an electronic output.

BACKGROUND

Operational notes pertaining to the treatment and care of a patientduring the course of a surgery are routinely prepared and compiled aftera surgical procedure. Operational notes may include a variety ofinformation including, for example, surgical actions, surgical tools,medical personnel, details of the procedures, or the results of theprocedures and the complications, if any, which the patient experienced.Additionally, the content of an operational note may includeobservations, interpretations, and recommendations of a treatingphysician, a consulting physician, or other medical personnel.Operational notes are typically organized into sections, such asaccording to anatomy, pathology, and/or the content or subjectsmentioned above.

However, there are a variety of disadvantages associated with thetraditional approaches to preparing an operational note. For example,typically someone other than the author of the operational note preparesthe transcript from the recording made by the physician. Discrepanciesbetween the author's and physician's term definitions or backgroundknowledge can then result in errors in the note that correspond toinaccurate and/or incomplete representations of events. Inaccuraciesand/or data omissions can impact not only immediate patientcare—particularly when the patient is in a critical condition—but alsolong-term healthcare costs. Highly variable, dynamic and/orunpredictable environments of a surgical procedure present challenges interms of how representations of the environments are to be processed tooutput data to reliably generate and update operational notes.

BRIEF SUMMARY

In some embodiments, a computer-implemented method is provided. Asurgical data set including surgical data collected during performanceof a surgical procedure is obtained. The surgical data includes one ormore data streams from the surgical procedure. A first surgical datastructure that corresponds to the surgical dataset is identified. Thefirst surgical data structure includes a plurality of nodes. Each nodeof the plurality of nodes represents a procedural state and a set ofprocedural metadata. Each node of the plurality of nodes are connectedvia at least one edge to at least one other node of the plurality ofnodes. A second surgical data structure that corresponds to the surgicaldataset is identified. The second surgical data structure includes aplurality of additional nodes. Each additional node of the plurality ofnodes are connected via at least one edge to at least one otheradditional node of the plurality of additional nodes. Each additionalnode of the plurality of additional nodes are connected via at least oneedge to at least one of the plurality of nodes of the first surgicaldata structure. A portion of the of the surgical dataset correspondingto a first node of the plurality of nodes from the first data structureand a corresponding second node of the plurality of additional nodesfrom the second data structure is determined based on the one or moredata streams included in the portion. One or more edges are identified.Each edge of the one or more edges connect the first node of the firstdata structure to the second node of the second surgical data structure.Each edge of the one or more edges is associated with additionalmetadata corresponding to the surgical procedure. An edge of the one ormore edges is selected based on the additional metadata associated withthe one or more edges. Electronic data associated with the portion ofthe surgical dataset is generated based on the selected edge. Electronicdata associating the electronic data with the portion of the surgicaldataset is output.

In some embodiments, a computer-implemented method is provided. One ormore data streams are received. Each data stream of the one or more datastreams having been generated at and received from an electronic deviceconfigured and positioned to capture data during a surgical procedure.The one or more data streams including a set of information units. Eachinformation unit of the set of information units corresponding to adifferent temporal association relative to other information units ofthe set of information units. Processing the one or more data streamsusing a multi-dimensional artificial-intelligence protocol based on thefirst data structure and the second data structure. The processingincluding repeatedly detecting new information units of the set ofinformation units received as part of the one or more data streams andprocessing each data portion of the new information unit by: extractingfeatures of the information unit to identify one or more features,wherein each of the one or more features corresponds to an incompletesubset of the data portion; classifying and/or localizing, for each ofthe one or more features and during a stage of the multi-dimensionalartificial-intelligence protocol, the feature as corresponding to aparticular object type of a set of predefined object types; determining,during the stage of the multi-dimensional artificial-intelligenceprotocol, a particular state of the set of procedural states based onthe classifying and/or localizing and at least some of the metadata inthe first data structure; identifying one or more propertiescorresponding to the one or more features; comparing the one or moreproperties to the metadata identified for the procedural state in thefirst data structure and the metadata identified for the second datastructure; determining, based on the comparison and one or morepredefined conditions and during another stage of the multi-dimensionalartificial-intelligence protocol, whether a procedural departureoccurred. A corresponding to a particular procedural instance is updatedto include a file update corresponding to the information unit. The fileupdate identifies the particular state, and the file update includes arepresentation of a result of at least part of the comparison when it isdetermined that a procedural departure occurred. The updated file isoutput.

In some embodiments, a computer-program product is provided that istangibly embodied in a non-transitory machine-readable storage medium.The computer-program product can include instructions configured tocause one or more data processors to perform operations of part or allof one or more methods disclosed herein.

In some embodiments, a system is provided that includes one or more dataprocessors and a non-transitory computer readable storage mediumcontaining instructions which, when executed on the one or more dataprocessors, cause the one or more data processors to perform operationsof part or all of one or more methods disclosed herein.

BRIEF DESCRIPTION OF THE DRAWINGS

The patent or application file contains at least one drawing executed incolor. Copies of this patent or patent application publication withcolor drawing(s) will be provided by the Office upon request and paymentof the necessary fee.

The present invention will be better understood in view of the followingnon-limiting figures, in which:

FIG. 1 shows a network for using data during a surgical procedure toidentify procedural states and generate and/or update an electronicoutput in accordance with some embodiments of the invention.

FIG. 2 shows a network for identifying procedural states in accordancewith some embodiments of the invention.

FIG. 3 shows an embodiment of a system for collecting data and producingan electronic output in accordance with some embodiments of theinvention.

FIG. 4 shows a collection of surgical environments potentially exposingsensitive information from a surgical procedure.

FIG. 5 shows an embodiment for a process of identifying sensitiveinformation and distorting and/or blurring a localized area foranonymization.

FIG. 6 shows an embodiment of a global surgical map for multipleinterconnected surgical data structures.

FIG. 7 shows an embodiment for processing surgical data using multipleinterconnected surgical data structures.

DETAILED DESCRIPTION

The ensuing description provides preferred exemplary embodiment(s) only,and is not intended to limit the scope, applicability or configurationof the disclosure. Rather, the ensuing description of the preferredexemplary embodiment(s) will provide those skilled in the art with anenabling description for implementing a preferred exemplary embodiment.It is understood that various changes may be made in the function andarrangement of elements without departing from the spirit and scope asset forth in the appended claims.

In some embodiments, methods and systems are provided to process asurgical dataset, represented in multiple interconnected surgical datastructures, which can be used to retrieve or generate pertinentinformation (e.g., corresponding to a surgical procedure) to generate(e.g., continuously update) an electronic output. The electronic outputcan be used to generate and/or update an operational note. Surgical datastructures, that represent various characteristics in a surgery andcorresponding information, can be locally retrieved or received. Each ofthe surgical data structures may include one or more connections betweeneach respective surgical data structure (e.g., interconnected nodes)that represent characteristics in a surgery and correspondinginformation. The surgical dataset can be processed in accordance withthe multiple interconnected surgical data structures associated with thesurgery to identify characteristics of a surgical procedure (e.g., aspecific procedural state, surgical tools, surgical actions, anatomicalfeatures, events, etc.). Metadata associated with the characteristics(e.g., which can include information associated with procedural state)can be retrieved (e.g., from the surgical data structure or from alocation identified by the surgical data structure) and stored from eachrespective data structure to generate an electronic output. In someembodiments, the electronic output may be transmitted to a centralizedhospital control center to provide additional information of theoperating room conditions including (for example) procurementinformation (e.g., disposable equipment, sanitary items, tools, etc.).

Each of the surgical data structures of the multiple interconnectedsurgical data structures may include one or more nodes that areconnected to nodes of another surgical data structure. The multipleinterconnected surgical data structures may include more than twosurgical data structures (e.g., more than three, more than four, morethan five, etc.). The interconnected surgical data structures caninclude relational metadata (e.g., metadata associated withcorresponding data structures) not available in a single data structure.For example, the interconnections between one or more surgical datastructures can include relational metadata that identifies specificevents during a surgical procedure. This relational metadata is uniqueto the connected surgical data structures. The metadata can be used togenerate and/or update an electronic output (e.g., at a discrete time,at multiple times or continuously) during a surgical procedure orafterwards. The electronic output, based on the metadata, can identify(for example) a patient and one or more medical providers involved in asurgery, a type of surgery, an underlying condition, a result of thesurgery, one or more states underwent throughout the surgery, a degreeto which (e.g., for each of the one or more states) one or more observedproperties matched one or more target properties for the state, one ormore surgical tools used (e.g., and an association of individual statesin which individual tools were used), and/or any departure from surgicaltargets.

Surgical data structures described herein may include intelligent systemthat collect and analyze operating room data and take actions thatanalyze the data through multiple interconnected data structures.Surgical data structures described herein may describe an appropriatesurgical route using the surgical data structure and a number of inputsincluding one or more user inputs, operating room video or audiostreams, smart surgical tools, accelerometer data from a surgeon orother operating room personnel, patient data, and medical records.Surgical data structures described herein determine the procedurallocation and a numerous other characteristics of the surgical procedure(e.g., surgical tools, anatomical features, spatial features, temporalinformation, etc.).

Each of the surgical data structures may also function independently. Insome cases, each of the surgical data structures can independentlyprocess the one or data streams from the surgical procedure. Forexample, a first data structure can identify procedural stages of asurgical procedure, a second data structure can identify surgical toolsin a surgical procedure, and a third data structure can identifysurgical actions in a surgical procedure. In portions of the surgicalprocedure that do not include a surgical tool, the one or more datastreams can be processed using the first data structure to identify thestage of surgery. Alternatively, the one or more data streams can beprocessed using the second data structure to identify the events duringa surgical procedure. In this instance, the electronic output may onlyrelate to one of the surgical data structures or a combination of datastructures (e.g., first and third data structure, first and second datastructure, second and third data structure, etc.).

In some embodiments, the surgical data can be processed in accordancewith multiple surgical data structures associated with the surgery toidentify characteristics of the surgical procedure (e.g., specificprocedural state, surgical actions, surgical tools, or risk). Proceduralmetadata associated with the surgical procedure can be retrieved (e.g.,from the surgical data structure or from a location identified by thesurgical data structure) from each surgical data structure, acorresponding interrelation (e.g., metadata associated with eachsurgical data structure) can be identified between each surgical datastructure, and transmitted.

In some embodiments, each of the surgical data structures can begenerated using data streams obtained from previous surgical procedures.In one instance, video streams from a previous surgical procedure can beprocessed (e.g., using image-segmentation) to identify, detect, anddetermine probabilities of a surgical procedure. The video streams canbe annotated to include information relevant to different portions ofthe surgical procedure to generate surgical data structures. Forexample, a video stream from an endoscopic procedure can be segmented toidentify surgical instruments during the procedure. The surgical datastructure can be generated by using training data with pixel-levellabels (i.e., full supervision) from the segmented endoscopic procedurevideo stream. In some aspects, generating a surgical data structure canbe produced using other methods. For example, a video stream from anendoscopic procedure can be processed to detect instruments by usingthree different processes: identification (e.g., identifying whichinstrument is present in the image), bounding box regression (e.g.,localizing each instrument in the image by finding a bounding box thatencloses them), and heatmap regression (e.g., probability maps of whereinstruments might be present). This information can be compiled togenerate a surgical data structure.

Each of the surgical data structures may include a plurality of nodesand a plurality of edges. Each edge of the plurality of edges may beconfigured to connect two nodes of the plurality of nodes. The nodes andedges may be associated with and/or arranged in an order thatcorresponds to a sequence in which various actions may be performedand/or specific surgical instruments in a surgical procedure. Each ofone more or all nodes in the surgical data structure and/or each of one,more or all edges in the surgical data structure may be associated withsurgical metadata

In some embodiments, each surgical data structure can include multiplenodes, each node representing particular characteristics of surgery(e.g., procedural state, state of a patient, surgical instruments,etc.). For example, in one data structure, a node can represent adiscrete physiological and procedural state of the patient during aprocedure (e.g., initial preparation and sterilization complete, skinincision made, bone exposed, etc.) and, in a second data structure, anode can represent a discrete surgical tool in the operating room duringa procedure (e.g., forceps, stapler, laparoscopic instruments, etc.).The nodes of the first data structure and the nodes of the second datastructure can be interconnected to provide relational metadata. Forexample, the relational metadata may represent a pose, anatomical state,sterility, a state of a patient, such as patient position, location ofan organ, location of a surgical instrument, location of an incision,etc.

Each surgical data structure can further include multiple edges, witheach edge connecting multiple nodes and representing a characteristic ofa surgical procedure. Each edge of the plurality of edges may beconfigured to connect two nodes of the plurality of nodes. The nodes andedges may be associated with and/or arranged in an order thatcorresponds to a sequence in which various actions may be performed in asurgical procedure. Each of one more or all nodes in the surgical datastructure and/or each of one, more or all edges in the surgical datastructure may be associated with procedural metadata. In some cases, anedge that connects two nodes of the plurality of nodes within a singledata structure can represent transition within that specific datastructure. For example, for a data structure representing proceduralstates, an edge can represent one or more surgical actions executed totransition between different nodes. In other embodiments, an edge canconnect two nodes of the plurality of nodes between two different datastructures. Each the edges connecting interconnecting nodes betweensurgical data structures may be associated with additional proceduralmetadata. Each edge can be associated with information about thesurgical action, such as an identification of the action, a timetypically associated with the action, tools used for the action, aposition of interactions relative to anatomy, etc. For example, for aninterconnected set of nodes between two data structures can poolmetadata from each respective data structure to represent additionalcharacteristics of a surgical procedure.

For example, a first data structure can include multiple nodes, eachnode representing a procedural state, and a second data structure caninclude multiple nodes, each node representing a surgical instrument.The multiple nodes from the first data structure can be connected (e.g.,via edges) to the multiple nodes of the second data structure. The edgesinterconnecting the nodes of each structure may include informationincluded in or identified by relational metadata. The edges betweennodes for each data structure may (for example) identify the proceduralstate (e.g., by identifying a state of a patient, progress of a surgery,etc.), identify an action that is being performed or is about to beperformed (e.g., as identified in an edge that connects a nodecorresponding to the procedural state with a node corresponding to anext procedural state), and/or identify one or more considerations(e.g., a risk, tools being used or that are about to be used, a warning,etc.).

In one embodiment, the one or more data streams are processed by each ofthe data structures (for example) to identify a procedural state,identify one or more surgical tools in the procedural state, and one ormore surgical actions in the surgical procedure. For example, one datastructure may identify surgical actions represented in a surgical datastructure and another data structure may identify surgical tools used ina stage of surgery. Each of the data structures can include multiplenodes that are connected by edges across surgical data structures. Basedon the identified nodes and related edges, (relational) metadata isavailed. The metadata can provide information regarding events thatoccurred during a procedure (for example) including surgical events,risk assessment, or detection of actions that lead to specific events.For example, based on the identified state, the data structures canidentify the spatial relation of a surgical tool to an anatomicalfeature over a period of time that may have caused damage or bleeding.This information can be an electronic output. The electronic output canbe stored as an operational note.

In some instances, a computer-implemented method is provided that uses amulti-dimensional artificial intelligence to process live or previouslycollected surgical data. The processing, using the multipleinterconnected structures, can result in both an identification ofparticular characteristics of the surgery and further a comparison ofthe particular characteristics to target characteristics. Informationcan be selected based on the comparison to generate or update anelectronic output. More specifically, surgical data can be received fromone or more data streams. Each of multiple epochs of the surgical datacan be processed to identify a procedural state in surgery. For example,each of the multiple epochs can correspond to a time interval (e.g.,such that a state is identified for each 20-second interval of thesurgical data). Thus, in some instances, a same state may be identifiedfor multiple consecutive epochs. Identifying one or more epochs of datacan include (for example) dividing or parsing continuous data and/orassigning individual non-continuous (e.g., discrete or semi-continuous)data sets and/or non-continuous data points to individual epochs. Forexample, a single data point (e.g., representing movement of aparticular surgical tool) can be associated with a time identified in asame communication that included the single data point or with a time atwhich the single data point was received, and the single data point canthen be assigned to an epoch corresponding to a time window thatincludes the time. Data assigned to a given epoch can then be processedto identify to which state (from amongst multiple predefined states) theepoch corresponds.

The state can be determined using a first data structure (e.g.,procedural-state data structure) that indicates, for each of themultiple predefined states, a set of state characteristics of the state.The set of state characteristics can include (for example) one or morerequired characteristics (that are definitively and reliably true forthe state), one or more possible characteristics (that may be true forthe state) and/or one or more exclusion characteristics (that cannot betrue for the state). It will be appreciated that, in some instances, astate may still be identified despite a lack of detecting a requiredcharacteristic. This situation may occur when the requiredcharacteristic is unreliably detectable. Thus, the first structure maybe configured to be evaluated based on one or more (e.g., predefined orlearned) probabilities. A probability may indicate a likelihood ofdetecting a given characteristic or a probability that a possiblecharacteristic is an actual characteristic of the state. Using detecteddata and the characteristics (and potentially the probabilities) of thedata structure, a score can be determined for each epoch and each state,and a state having a highest score for a given epoch can be assigned tothe epoch.

The data structure can also include order constraints and/or orderprobabilities, which can indicate (for example) a possibility and/orlikelihood of transitioning from one state to another state. This orderdata can be used to constrain states considered for assignment for agiven epoch based on a state assignment from one or more previousepochs. Further, the order data can be used to (for example) weight,bias or inform a state assignment. Additional detail of data structuresto be used for state assignment can be found in U.S. Pat. No. 9,788,907,filed on Feb. 20, 2017, which is hereby incorporated by reference in itsentirety for all purposes.

The surgical data assigned to an epoch can be used not only to identifya particular state that corresponds to the data epoch but also (oralternatively) to characterize particular actions, events or othercharacteristics of the epoch, according to a second data structure. Forexample, each of the data structures can be used to detect and/orclassify (or localize) an object in an epoch, identify one or moreproperties of the object (e.g., Cartesian position, angular position,movement direction and/or operation type) and compare the object typeand/or one or more properties to one or more corresponding targetparameters defined for the state.

The detected data, inferred information and/or comparison results can beused to facilitate provision of feedback (e.g., at a discrete time, atmultiple times or continuously) during a surgical procedure orafterwards. In some instances, the feedback is provided in an electronicoutput. The electronic output can identify (for example) a patient andone or more medical providers involved in a surgery, a type of surgery,an underlying condition, a result of the surgery, one or more statesunderwent throughout the surgery, a degree to which (e.g., for each ofthe one or more states) one or more observed properties matched one ormore target properties for the state, one or more surgical tools used(e.g., and an association of individual states in which individual toolswere used), and/or any departure from surgical targets. The electronicoutput can be used to generate one or more user-level reports (e.g.,corresponding to a particular medical professional) and/or one or moremanagement-level reports (e.g., corresponding to a medical team and/orinstitution). For example, the electronic output can be availed to adevice of a decision maker (e.g., a surgeon) and/or a group or panel ofreviewers, such that real-time decisions or high-level approaches can beadjusted.

In some cases, a plurality of sensors (e.g., cameras, microphones, ormedical equipment) can capture one or more data streams during asurgical operation. It will be appreciated that, as used herein, a datastream may correspond to data that is continuously transmitted from asource to a target over a time window, data that is intermittently(e.g., periodically or upon-detection) transmitted from a source to atarget over a time window, a discrete transmission from a source to atarget and/or multiple discrete transmissions. For example, a datastream may include a video feed of a surgery, a set of regularly spacedand discrete vital-sign measurements, discrete detection eventsindicating times in which a particular surgical tool was activated, aset of controlled video feeds with start and end times occurring withina total surgical time and being controlled by a user, etc.

The one or more data streams may be transmitted (e.g., as it iscollected or after collection of all data from a procedure) to aprocessing unit that may process the data in real-time or store the datafor subsequent processing. The one or more data streams can be processedusing a multi-dimensional artificial intelligence in order toautomatically generate or update an electronic output associated with aparticular surgery and/or an electronic medical record (EMR) associatedwith a particular patient. The note or record can be generated orupdated to include a report of multiple observations detected inassociation with during a surgical procedure including (for example)instruments used during the surgical procedure (e.g., and/or states inwhich the instruments were used and/or manners in which the instrumentswere used), particular steps completed (e.g., in association withparticular states), and/or error information (e.g., reflectingdiscrepancies between observed characteristics and targetcharacteristics, such as detecting that a stapler or clip was not usedduring a particular state). Target characteristics can be defined to(for example) correspond to high or optimal action precision, ergonomicoptimization, action efficacy and/or patient safety.

One or more types of artificial-intelligence techniques can be used toprocess the surgical data to detect and identify objects of relevancewithin the surgical data. For example, multiple interconnected datastructures as described herein can be used to process the surgical datato detect and identify objects of relevance. A data structure (e.g., aprocedural-state data structure) associated with a set of proceduralstates for a surgical procedure can include a set of characteristicprocedural metadata associated with each procedural state from a set ofprocedural states. The multi-dimensional artificial intelligence, in onestage, can process input data to (for example) detect particularcharacteristics of the data. In some instances, the detection isperformed at an epoch level, such that input data is assigned toindividual epochs and then input data for each epoch is processed toidentify one or more particular characteristics for the epoch. Forexample, the processing may include extracting features from a videodata to identify discrete objects (and/or discrete tool usages and/oractions) and classifying and/or localizing each object (and/or toolusage and/or action). The multi-dimensional artificial intelligence, inanother stage, can compare the one or more characteristics to the one ormore target characteristic metadata identified for the procedural statein the multiple interconnected data structures to determine whether, anextent to which and/or a type of a procedural departure occurred.

The one or more data streams can be generated and received from one ormore electronic devices configured and positioned to capture live orpreviously collected data during a surgical procedure. The one or moredata streams can include (for example) time-varying image data (e.g., avideo stream from different types of cameras), audio data (e.g.,instructions from medical personnel, for example, a surgeon), and/orelectrical signals (e.g., data from equipment in surgical procedure).The data streams may vary depending on the type of operation. Forexample, which types of data streams are received may depend on a typeof surgery (for example) including open surgery techniques, laparoscopicsurgery techniques, or microscopic surgery techniques. Generally, somedata streams available during a surgical procedure (for example) caninclude video data (e.g., in-light camera, laparoscopic camera, wearablecamera, microscope, etc.), audio data (e.g., near-field sounds,far-field sounds, microphones, wearable microphones, etc.), signals frommedical devices (e.g., anesthesia machine, pulse and blood pressuremonitors, cardiac monitors, navigation systems, etc.) and/or variousinputs from other operating room equipment (e.g., pedals, touch screens,etc.). Using one or more data streams, the methods and systems canidentify progression through various surgical states and/or compareactual observed events throughout the surgery to target or expectedresults (e.g., for the surgery and/or individual states). Thisassessment (and/or a processed version thereof) can be identified in ageneration or update of an electronic output (e.g., an operational noteand/or an electronic medical record).

The multi-dimensional artificial intelligence can be used to process(e.g., in real-time or offline) one or more data streams (e.g., videostreams, audio streams, RFID data, etc.). The processing can include(for example) detecting and characterizing one or more features withinvarious instantaneous or block time periods. Each feature can bedetected (for example) using feature extraction and can be classifiedand/or localized (e.g., finding the position of a specific feature). Thefeature(s) can then be used to identify a presence, position and/or useof one or more objects, characterize a representation of each object(e.g., in terms of its orientation, position, use state, movementcharacteristic, etc.), which can be used (e.g., in combination withother object detections and/or characterizations) to identify a statewithin a workflow (e.g., as represented via a surgical data structure),compare one or more properties (e.g., orientation, position, and/ormovement) of the one or more identified features to one or more targetproperties for the state, etc.).

Techniques described herein can facilitate various technical advantages.In some instances, more efficient data storage and/or more efficientquerying can be facilitated by identifying data properties and targetdiscrepancies that can more efficiently represent complex and extendeddata from multiple sources. Further, identifying, storing andcommunicating data and results in association with procedural statesincreases the relevance of the data, which can thus result in storageand procedural efficiencies. For example, the state-based approach canfacilitate efficient implementation of a query to identify a statewithin a particular procedure in which a prevalence of deviation betweentarget characteristics and actual characteristics was maximized acrossstates of the procedure. As another example, the state-based approachcan facilitate efficient querying to detect a distribution acrosspossible action implementations corresponding to a given state in theprocedure. As yet another example, the state-based approach canfacilitate identifying a prominent predictor of an early-state action ona later-state result. Each of these queries may have been difficult toimpossible if surgical data was stored in a non-state-based manner.

FIG. 1 shows a network for using live or previously collected data froma surgical procedure to generate and/or update an electronic output inaccordance with some embodiments of the invention. Network 100 includesa surgical operating system 105 that collects one or more data streamsfrom a surgical procedure. Surgical operating system 105 can include(for example) one or more devices (e.g., one or more user devices and/orservers) located within and/or associated with a surgical operating roomand/or control center. Network 100 further includes a multi-dimensionalartificial intelligence system 110 that processes the one or more datastreams to identify a procedural state, to determine one or moreproperties of the actual data (e.g., in relation to the proceduralstate) and to compare the one or more properties to one or more targetproperties for the procedural state, which is used to generate acorresponding output. In some embodiments, network 100 can also processthe one or more data streams, using the multi-dimensional artificialintelligence system 110, to anonymize (e.g., distort, blur, or color)the one or more data streams. For example, the network 100 can store thedata so as to anonymize the data and/or to obscure and/or encryptparticular (e.g., private or identifying) features. In some embodiments,surgical data (or a processed version thereof, such as timestamped stateidentification) can be preserved for anonymization. It will beappreciated that the multi-dimensional artificial intelligence system110 can include one or more devices (e.g., one or more servers), each ofwhich can be configured to include part or all of one or more of thedepicted components of multi-dimensional artificial intelligence system110. In some instances, part or all of multi-dimensional artificialintelligence system 110 is in the cloud and/or remote from an operatingroom and/or physical location corresponding to part or all of surgicaloperating system 105.

Surgical operating system 105 can include (for example) one or moredevices (e.g., one or more user devices and/or servers) located withinand/or associated with a surgical operating room and/or control center.In some embodiments, the surgical data from the surgical operatingsystem 105 can include digital video data, which may include acontinuous signal that represents moving visual images. The surgicaldata may (e.g., alternatively or additionally) include audio data,representations of one or more inputs received at a device (e.g., avoice command, a motion command or input received at a keyboard or otherinterface component), data from a smart surgical tool (e.g., indicativeof a location, movement and/or usage), and so on. The surgical data caninclude data collected from a single device or from multiple devices.The surgical data can include data collected over a time period (e.g., apredefined time increment, since a previous state-transition time orprocedure initiation time, or a time of an entire surgical procedure).The surgical data may include raw data collected during the surgery or aprocessed version thereof (e.g., to reduce noise and/or transform asignal, such as transforming accelerometer data collected at a headsetto a movement command indicating that the surgical procedure hasprogressed to a next step).

Multi-dimensional artificial intelligence system 110 can access a datastructure. The data structure can be associated with a set of proceduralstates for a surgical procedure. The data structure can identifycharacteristics for each procedural state of the set of proceduralstates. Each procedural state may be associated with a set ofcharacteristic procedural metadata that indicates one or more datacharacteristics that correspond to the procedural state and one or moretarget characteristic metadata. Metadata and target characteristicmetadata can identify (for example) particular tools, anatomic objects,medical personnel, actions being performed in the instance, and/orsurgical states. Multi-dimensional artificial intelligence system 110can use the corresponding metadata and/or target characteristic metadatato define one or more parameters of the model so as to learn (forexample) how to transform new data to identify features of the typeindicated by the metadata and/or target characteristic metadata.

In some embodiments, the data structure may be received from anotherremote device (e.g., cloud server), stored and then retrieved prior toor during performance of a surgical procedure. The data structure canprovide precise and accurate coded descriptions of complete surgicalprocedures. The data structure can describe routes through surgicalprocedures. For example, the data structure can include multiple nodes,each node representing a procedural state (e.g., corresponding to astate of surgery). The data structure can further include multipleedges, with each edge connecting multiple nodes and representing asurgical action.

In some instances, multi-dimensional artificial intelligence system 110can access a data structure and accordingly configure an artificialintelligence system. The artificial intelligence system can include, forexample, a machine-learning model trained using the surgical datastructure. The trained machine-learning model can include, for example,a convolutional neural network adaptations, adversarial networks,recurrent neural networks, deep Bayesian neural networks, or other typeof deep learning or graphical models.

Multi-dimensional artificial intelligence system 110 can process the oneor more data streams from the surgical operating system 105. In a firststage, multi-dimensional artificial intelligence system 110 can processthe one or more data streams to perform state detection 120. For statedetection 120, a procedural state is determined from the one or moredata streams. The one or more data streams may include a set ofinformation units. Each information unit of the set of information unitsmay correspond to a different temporal association relative to otherinformation units of the set of information units. Multi-dimensionalartificial intelligence system 110 can repeatedly detect new informationunits of the set of information units received as part of the one ormore data streams and process each data portion of the new informationunit.

During state detection 120, the multi-dimensional artificialintelligence system 110 can generate feature-extraction data 122 byfeature extraction or localization (e.g., segmenting) each data portionof the new information unit to identify one or more features. Each ofthe one or more features of the feature-extraction data may correspondto an incomplete subset of the data portion. For example, the newinformation unit can correspond to an incomplete subset of time-varyingimage data (e.g., a video stream from different types of cameras), suchas a set of pixels. The multi-dimensional artificial intelligence canextract features from the time-varying image data to identify and/orcharacterize each of the one or more objects (e.g., medical personnel,tools, anatomical objects) that are depicted in the image or video. Thecharacterization can (for example) indicate a position of the object inthe object (e.g., a set of pixels that correspond to the object and/or astate of the object that is a result of a past or current userhandling). Metadata and feature-extraction data can identify (forexample) particular tools, anatomic objects, actions being performed inthe simulated instance, and/or surgical states.

Multi-dimensional artificial intelligence system 110 can perform aclassification and/or localization of the feature-extraction data 122 toobtain classification and/or localization data 124. For example, themulti-dimensional artificial intelligence system 110 can classify thefeature-extraction data 122 as corresponding to a particular object typeof a set of predefined object types in a classification 124 during statedetection 120. The artificial intelligence can determine (e.g.,state-detection stage) a particular state based on the classificationand at least some of the sets of characteristic procedural metadata inthe data structure. In some instances, the multi-dimensional artificialintelligence system can be configured to classify an image using asingle image-level classification or by initially classifying individualimage patches. The classifications of the patches can be aggregated andprocessed to identify a final classification.

The state detection 120 can use the output from the classificationand/or localization to determine a particular state of a set ofprocedural states based on the classification and/or localization data124 and at least some of the sets of characteristic metadata in the datastructure. For example, information from the data structure can identifya set of potential states that can correspond to part of a performanceof a specific type of procedure. Different procedural data structures(e.g., and different machine-learning-model parameters and/orhyperparameters) may be associated with different types of procedures.The data structure can include a set of nodes, with each nodecorresponding to a potential state. The data structure can includedirectional connections between nodes that indicate (via the direction)an expected order during which the states will be encountered throughoutan iteration of the procedure. The data structure may include one ormore branching nodes that feeds to multiple next nodes and/or caninclude one or more points of divergence and/or convergence between thenodes. In some instances, a procedural state indicates a proceduralaction (e.g., surgical action) that is being performed or has beenperformed and/or indicates a combination of actions that have beenperformed. In some instances, a procedural state relates to a biologicalstate of a patient.

Each node within the data structure of the multiple interconnected datastructures can identify one or more characteristics of the state. Thecharacteristics can include visual characteristics. In some instances,the node identifies one or more tools that are typically in use oravailed for use (e.g., on a tool tray) during the state, one or moreroles of people who are performing typically performing a surgical task,a typical type of movement (e.g., of a hand or tool), etc. Thus, statedetection 450 can use the feature-extraction data 122 generated bymulti-dimensional artificial intelligence system 110 (e.g., thatindicates the presence and/or characteristics of particular objectswithin a field of view) to identify an estimated node to which the realimage data corresponds. Each of the surgical data structures may includeone or more interconnected nodes between each respective surgical datastructure (e.g., interconnected nodes) that represent metadata (e.g.,characteristics) in a surgical procedure and corresponding information.Identification of the node(s) (and/or state) can further be based uponpreviously detected states for a given procedural iteration and/or otherdetected input (e.g., verbal audio data that includes person-to-personrequests or comments, explicit identifications of a current or paststate, information requests, etc.) from multiple interconnected datastructures.

Multi-dimensional artificial intelligence system 110 can include a modelexecution 130. During model expectation 130, the multi-dimensionalartificial intelligence system 110 processes the feature-extraction data122 to determine identification data 132. The multi-dimensionalartificial intelligence system 110 identifies one or more propertiescorresponding to the one or more features. For example, the one or moreproperties may identify one or more sets of characteristics for the oneor more features. An identification is made as to which characteristicsare to be specifically fixed and/or varied (e.g., in a predefinedmanner). The identification can be made based on (for example) inputfrom a client device, a distribution of one or more characteristicsacross the base images and/or videos and/or a distribution of one ormore characteristics across other real image data.

Using the identification data 132 in the one or more extracted features,the model execution 130 generates comparison data 134 for each of theone more features. The multi-dimensional artificial intelligence maygenerate the comparison data 134 by comparing the one or more propertiesto the one or more target characteristic metadata identified for theprocedural state in the data structure. In some instances, the modelexecution system includes or is associated with a preprocessing (e.g.,intensity normalization, resizing, etc.) that is performed prior toextracting features from the image(s). An output of the execution systemcan include feature extraction data that indicates which (if any) of adefined set of objects are detected within the image data, a locationand/or position of the object(s) within the image data, and/or state ofthe object. Based on the comparison and one or more predefinedconditions during another stage of the multi-dimensionalartificial-intelligence protocol, the model execution outputs whether aprocedural departure occurred.

The state detection output 126 and the model execution output 130 can betransmitted to note generation 140. The note generation 140 can use thestate detection output 126 to identify the particular state of surgery.The note generation 140 can then use the state detection output 136 toupdate a file. The output generation for each stage of themulti-dimensional artificial intelligence can generate and/or retrieveinformation associated with the state and/or potential next events. Forexample, the information can include details as to warnings and/oradvice corresponding to current or anticipated procedural actions. Theinformation can further include one or more events for which to monitor.The information can identify a next recommended action.

FIG. 2 shows a data-processing flow 200 for processing live orpreviously collected surgical data in accordance with some embodimentsof the invention. In some embodiments, systems and methods used toprocess data to identify a real-time state of a surgery, represented ina surgical data structure, which can be used to generate an updated filebased on a comparison with one or more data streams from a data stream(e.g., corresponding to a current or subsequent action). Initially, theprocess may access a surgical data structure 205. For example, thesurgical data structure may be received from another remote device(e.g., cloud server), stored and then retrieved prior to or duringperformance of a surgical procedure. The surgical data structure canprovide precise and accurate coded descriptions of complete surgicalprocedures. The surgical data structure 205 can describe routes throughsurgical procedures. For example, the surgical data structure 205 caninclude multiple nodes, each node representing a procedural state (e.g.,corresponding to a state of a patient).

In some embodiments, the surgical data structure 205 can be associatedwith a set of procedural states for a surgical procedure. The set of setof procedural states can identify, for each procedural state of the setof procedural states, a set of characteristic procedural metadata thatindicates one or more data characteristics that correspond to theprocedural state and one or more target characteristic metadata. Themetadata and/or target characteristic metadata can identify (forexample) particular tools, anatomic objects, actions being performed inthe simulated instance, and/or surgical states.

During a surgery, live or previously collected surgical data 210 can becollected (e.g., from a sensor at a wearable device, smart surgicalinstrument, etc.). The surgical data 210 may include one or more datastreams including a set of information units. Each information unit ofthe set of information units corresponds to a different temporalassociation relative to other information units of the set ofinformation units. For example, one or more data streams from a surgicalprocedure can be collected from one or more cameras, one or moremicrophones, or one or more electrical signals (e.g., from medicalequipment). The surgical data 210 may include (for example) user inputs,a video stream, or operating room data. For example, user inputs mayinclude manual inputs directly provided by operating room personnel(e.g., via a wearable device or non-wearable device located inside ornear an operating room). For example, operating room personnel mayinclude the performing surgeon, a nurse, an anesthetist, or remotesurgeon watching the procedure. Inputs may include gesture, voice,another form of hands-free sterile interaction and even direct contactwith a computer user-interface. As another (additional or alternative)illustration, surgical images may be recorded, such as those captured onvideo from a head-mounted surgeon camera. As yet another (additional oralternative) illustration, surgical data may include other operatingroom data such as; time elapsed in procedure, nodes already passed bythe users (e.g., surgeon, nurse or team member), identified surgicalinstruments used or in use, personnel present in the operating room anduser movements measured from accelerometers in a guidance system headsetor other system-connected wearable devices.

A multi-dimensional artificial intelligence protocol 215 can receive thesurgical data 210. The multi-dimensional artificial intelligenceprotocol 215 segments the surgical data 210 into one or more segments togenerate feature-extraction data 220 for each data stream of thesurgical data. The one or more segments correspond to an incompletesubset of the data portion. In some embodiments, the artificialintelligence segments the surgical data 210 using feature extraction.Anatomic feature extraction can be performed on the surgical images todetect an anatomic state, anatomic pose, or any other attribute possibleto derive from video that can be used later to determine a preciseprocedural state. Additional data such as user input or operating roomdata may assist with feature extraction. The feature extractionalgorithm used to detect a one or more properties from the surgical datamay include machine learning iterations. In particular, if the firstimplementation of the systems and methods described herein requiresmanual navigation through the procedure by the user (e.g., surgeon,nurse or team member), the resulting dataset of live or previouslycollected surgical images and associated navigation commands can be usedas an effective algorithm training resource using machine learningsoftware. In other embodiments, a component may be associated with auser input or operating room data.

In a first stage, a multi-dimensional artificial intelligence canidentify the current procedural state 225 from the feature-extractiondata 220. Based on each of the one or more segments from thefeature-extraction data 220, each segment can be classified and/orlocalized as corresponding to a particular object type of a set ofpredefined object types. The multi-dimensional artificial-intelligenceprotocol can determine a particular state 225 of the set of proceduralstates based on the classification and/or localization and at least someof the sets of characteristic procedural metadata in the data structure.

In some embodiments, temporal information is identified for a part ofthe data streams associated with the procedural state. The temporalinformation may include (for example) a start time, end time, durationand/or range. The temporal information may be absolute (e.g., specifyingone or more absolute times) or relative (e.g., specifying a time from abeginning of a surgery, from a beginning of a procedure initiation,etc.). The temporal information may include information defining a timeperiod or time corresponding to the video data during which it isestimated that the surgery was in the procedural state.

The temporal information may be determined (for example) based oninformation associated with the state identification. For example, ifsurgery data is divided into data chunks, and each data chunk isassociated with a procedural state, the temporal information mayidentify a start and end time that corresponds to (for example) a starttime of a first data chunk associated with a given procedural state andan end time of a last data chunk associated with a given proceduralstate, respectively. As another example, if discrete data points arereceived that are indicative of state transitions, the temporalinformation may include times associated with two consecutive datapoints. As yet another example, a data set may be analyzed to detectstate transitions (e.g., by detecting activation/inactivation of aparticular smart tool, detecting a time period during which a particularvisual characteristic is detected in video data, detecting times atwhich an audio command is detected in an audio signal that indicatesthat a state is transitioning, etc.). It will be appreciated thattransitions between different states may have a same or differenttransition characteristic.

The multi-dimensional artificial intelligence can output 230 theidentified procedural state. In some embodiments, the output 230 of theidentified procedural state can include (for example) feature-extractiondata (e.g., that indicates which object(s) are present within the imagedata and/or corresponding position information) and/or an identificationof a (current and/or predicted next) procedural state. If the outputdoes not identify a procedural state, the output may be furtherprocessed (e.g., based on procedural-state definitions and/orcharacterizations as indicated in a data structure) to identify a(current and/or predicted next) state.

In a second stage, the multi-dimensional artificial intelligence cangenerate identification data 235. The identification data 235 identifiesone or more properties corresponding to the feature-extraction data 220.For example, the one or more properties identified in thefeature-extraction data 220 can include one or more objects (e.g.,medical personnel, tools, anatomical objects) that are depicted in animage or video. The multi-dimensional artificial intelligence cancompare the identification data 235 corresponding to thefeature-extraction data with the one or more target characteristicmetadata identified for the procedural state in the data structure.Based on the comparison and one or more predefined conditions, themulti-dimensional artificial intelligence can determine whether aprocedural departure occurred 240.

An output of an updated file 245 indicating the departure from theprocedural state is generated based on the identified state. The output245 can include (for example) information and/or recommendationsgenerally about a current state, information and/or recommendationsbased on live data and the current state (e.g., indicating an extent towhich a target action associated with the state is being properlyperformed or identifying any recommended corrective measures), and/orinformation and/or recommendations corresponding to a next action and/ornext recommended state. The output 245 can be availed to update anelectronic output 250. For example, the electronic output 250 can betransmitted to a user device within a procedure room or control center.

The electronic output 250 may be transmitted (e.g., in association withan identifier of the video data or surgical procedure) to storage oranother device. In some instances, the temporal information is used toannotate and/or assess video data. For example, it may be determinedwhether a surgical team traversed through an approved set of actions. Asanother example, a video may be annotated with captions to identify whatsteps are being performed (e.g., to train another entity).

FIG. 3 shows an embodiment of a system 300 for collecting live orpreviously collected data and/or presenting data corresponding to statedetection, object detection and/or object characterization performedbased on executing a multi-dimensional artificial intelligence. System300 can include one or more components of a procedural control system.

Computing device 360 can be placed inside the operating room or worn bya member of the operating room (e.g., surgeon, medical assistant, nurse,etc.) to capture data steams (e.g., video content) of the surgicalenvironment. The data can include image data (which can, in someinstances, include video data) and/or other types of data. For example,in laparoscopic or microsurgery procedures, computing device 360 maycapture data streams from video sources, such as a laparoscopic stack ora surgical monitor (collectively, 335), with video outputs. The data canbe transmitted to a computing device 360 via a wired connection or awireless connection. In some embodiments, the computing device 360 maybe wirelessly connected. The computing device 360 can collect data froma number of sources including (for example) a surgeon mounted headset310, a first additional headset 320, a second additional headset 322,surgical data 350 associated with a patient 312, an operating roomcamera 334, and an operating room microphone 336, and additionaloperating room tools not illustrated in FIG. 3. Local server 370receives the data from the computing device 360 over a connection 362(e.g., wired or wireless) and a surgical data structure from a remoteserver 380.

In some instances, the computing device 360 can process the data (e.g.,to identify and/or characterize a presence and/or position of one ormore tools using a trained machine-learning model, to identify aprocedural state using a trained machine-learning model or to train amachine-learning model). The computing device 360 can process themetadata corresponding to a procedural state identified as correspondingto live data and generate real-time guidance information for output tothe appropriate devices. Also, local server 370 can include one or morecomponents of the machine-learning processing system. Local server 370can process the metadata corresponding to a procedural state identifiedas corresponding to live data and generate real-time guidanceinformation for output to the control center 372.

The computing device 360 can be in contact with and synced with a remoteserver 380. In some embodiments, remote server 380 can be located in thecloud 306. In some embodiments, remote server 380 can process the livedata (e.g., to identify and/or characterize a presence and/or positionof one or more tools using a trained machine-learning model, to identifya procedural state using a trained machine-learning model or to train amachine-learning model). Remote server 380 can include one or morecomponents of the machine-learning processing system. Remote server 380can process the metadata corresponding to a procedural state identifiedas corresponding to live data and generate real-time guidanceinformation for output to the appropriate devices in operating room 302.

A global bank of surgical procedures, described using surgical datastructures, may be stored at remote server 380. Therefore, for any givensurgical procedure, there is the option of running system 300 as alocal, or cloud-based system. The computing device 360 can create asurgical dataset that records data collected during the performance of asurgical procedure. The computing device 360 can analyze the surgicaldataset or forward the surgical dataset to remote server 380 upon thecompletion of the procedure for inclusion in a global surgical dataset.In some embodiments, the computing device 360 can anonymize the surgicaldataset in real-time or up the completion of the procedure. System 300can integrate data from the surgical data structure and sort guidancedata appropriately in the operating room using additional components.

In certain embodiments, surgical guidance, retrieved from the surgicaldata structure, may include more information than necessary to assistthe surgeon with situational awareness. The system 300 may determinethat the additional operating room information may be more pertinent toother members of the operating room and transmit the information to theappropriate team members. Therefore, in certain embodiments, system 300provides surgical guidance to more components than a conventionaldisplay 330.

In certain embodiments, surgical guidance, retrieved from the surgicaldata structure, may include more information than necessary to assistthe surgeon with situational awareness. The system 300 may determinethat the additional operating room information may be more pertinent toother members of the operating room and transmit the information to theappropriate team members. Therefore, in certain embodiments, system 300provides surgical guidance to more components than a conventionaldisplay 330.

In the illustrated embodiment, mobile devices 331, such as smartphonesand tablets, and wearable devices, such as a surgeon's headset 310, afirst additional headset 320 and a second additional headset 322, areincluded in the system 300. Other members of the operating room team maybenefit from receiving information and surgical guidance derived fromthe surgical data structure on the mobile and wearable devices. Forexample, a surgical nurse wearing first additional headset 320 or havinga mobile device 331 in the close vicinity may benefit from guidancerelated to procedural steps and possible equipment needed for impendingsteps. An anesthetist wearing second additional headset 322 or having amobile display 331 in the close vicinity may benefit from seeing thepatient vital signs in the field of view. In addition, the anesthetistmay be the most appropriate user to receive the real-time riskindication as one member of the operating room slightly removed fromsurgical action.

Various peripheral devices can further be provided, such as conventionaldisplays 330, transparent displays that may be held between the surgeonand patient, ambient lighting 332, one or more operating room cameras334, one or more operating room microphones 336, speakers 340 andprocedural step notification screens placed outside the operating roomto alert entrants of critical steps taking place. These peripheralcomponents can function to provide, for example, state-relatedinformation. In some instances, one or more peripheral devices canfurther be configured to collect image data.

The computing device 360 may use one or more communications networks tocommunicate with operating room devices including (for example) wiredconnections (e.g., Ethernet connections) or various wireless protocols,such as IrDA™, Bluetooth™, Zigbee™, Ultra-Wideband, and/or Wi-Fi. Insome embodiments, existing operating room devices can be integrated withsystem 300. To illustrate, once a specific procedural location isreached, automatic functions can be set to prepare or change the stateof relevant and appropriate medical devices to assist with impendingsurgical steps. For example, operating room lighting 332 can beintegrated into system 300 and adjusted based on impending surgicalactions indicated based on a current procedural state.

In some embodiments, system 300 may include a centralized hospitalcontrol center 372 and a central hospital local server 370. The controlcenter 372 through the hospital local server 370 may be connected toone, more or all active procedures and coordinate actions in criticalsituations as a level-headed, but skilled, bystander. Control center 372may be able to communicate with various other users via user-specificdevices (e.g., by causing a visual or audio stimulus to be presented ata headset) or more broadly (e.g., by causing audio data to be output ata speaker in a given room 302.

In some instances, methods and systems are provided for performinganonymization of one or more data streams from the surgical procedure inan real-time process or an offline process. In some embodiments, thecomputing device 360 or a remote server 380 can anonymize and store theone or more data streams from a surgical procedure. Data streams (e.g.,video streams) from a surgical procedure contain sensitive orconfidential information such as patient identification, voice data,facial features, and other sensitive personal information about thepatient and/or operating room personnel. In some embodiments, the methodincludes anonymizing and protecting the identity of all medicalprofessionals, patients, distinguishing objects or features in amedical, clinical or emergency unit. The methods and systems can detectfacial features, objects, or features in a medical, clinical oremergency unit and distort or blur or colorize (e.g., black) or removethe image of the distinguishing element. In some embodiments, the extentof the distortion/blur/colorization is limited to a localized area,frame by frame, to the point where identity is protected withoutlimiting the quality of the analytics.

FIG. 4 shows a collection 400 of surgical environments potentiallyexposing sensitive information from a surgical procedure. The datastreams from a surgical procedure can be video data 410. The video data410 from a surgical procedure can be processed in a machine-learningmodel to anonymize and/or eliminate information through theidentification and removal of (for example) tagged information. In someembodiments, a deep-learning model (e.g., Faster RCNN) can be used toanonymize data streams from a surgical procedure. For example, the modelcan detect and blur faces in the data streams to preserve anonymity. Theanonymized data streams can be stored and used for a wide range ofpurposes (for example) documentation, teaching, and surgical datascience.

The data streams can be anonymized prior to storage on a server. In someembodiments, Convolutional Neural Networks (CNN) can be used fordetection, pose estimation, and emotion prediction. The introduction oflarge datasets has enhanced robustness and efficiency of deep learningmodels leading to developments of recurrent CNN (RCNN), Fast-RCNN, andFaster Regions with Convolutional Neural Networks (Faster-RCNN).

In some embodiments, Faster-RCNNs can process the data streams toanonymize (for example) video data captured during a surgical procedure.However, challenges exist with these models for use in a surgicalenvironment because the data needs to be adapted to process a pluralityof variables, e.g., masked faces, surgical caps, lighting variability,etc. For example, faces in the operating room are very different thanthose found in traditional datasets used to train machine-learningmodels due to masks, caps, and surgical magnifying glasses. Detectingsuch features require model adaptation. In some embodiments, the methodsfor anonymization provide a sliding window for temporal smoothing tohave a higher chance of detecting any missed information (e.g., a falsenegative). In some embodiments, a new dataset can be used to train amachine-learning model to account for variables in a surgicalenvironment, and can be fine-tuned to achieve higher detection rates.For example, datasets for facial recognition used for trainingmachine-learning models can be adapted (e.g., labeled surgicalenvironment data) to fine-tune a machine-learning model.

FIG. 5 shows one example of identifying sensitive information (e.g.,facial features) and augmenting a localized area (e.g., distort, blur,or black-out), frame by frame, to the point where identity is protectedwithout limiting the quality of the analytics. Specifically, FIG. 5shows an anonymized frame 500 of a video stream. In one example, frame500 of a video stream is anonymized using Faster-RCNNs. It is alsocontemplated that the frames of a video stream can be processed usingnumerous other deep-learning models (e.g., convolutional neuralnetworks, recurrent neural networks, etc.). The aforementionedFaster-RCNN uses a regional proposal network that estimates boundingboxes around an input data stream (e.g., input image). For example, thegreen bounding box 510 and the pink bounding box 520 describes theground truth and detected face, respectively. As seen in the image, theanonymization has occurred given the area above the mask was detectedrepresented by the green bounding box 510. The detected region is lessthan half the area of the annotated face.

In some embodiments, voice and/or voice alteration processes can be usedto anonymize and protect the identity of all medical professionals,patients, distinguishing objects or features in a medical, clinical oremergency environment. This software may implement methods andtechniques running on hardware in a medical, clinical or emergencyenvironment to alter voices, conversations and/or remove statements ofeveryday language to preserve the identity of the speaker while at thesame time maintaining the integrity of the input stream so as to notadversely impact the quality of the analytics.

Eliminating sensitive data involves the anonymizing of data through theidentification and removal of (for example) tagged information. Forexample, information such as patient identification, voice data, facialfeatures, and other sensitive personal information about the patientand/or operating room personnel can be eliminated from the one or moredata streams. To address privacy concerns, the artificial intelligencesystem can anonymize data in real time and the anonymized data streamsare stored. In some instances, the one or more data streams can beprocessed at a central server to anonymize data in real time. Theanonymization can include (for example) discarding speech used in theoperating room and only storing sounds that are associated withparticular surgical steps.

In some embodiments, live surgical data (or a processed version thereof,such as timestamped state identification) can be preserved for apost-hoc analysis (e.g., off-line analysis). To improve the securitycorresponding to the live data, the data may be stored so as toanonymize the data and/or to obscure and/or encrypt particular (e.g.,private or identifying) features. Live surgical data may be processed toidentify current procedural states throughout the surgery.

FIG. 6 illustrates an embodiment of a surgical map 600 includingmultiple data structures 604, 606, 608. In some embodiments, themultiple data structures 604, 606, 608 described herein include a globalsurgical graph 602 that may include a map (e.g., nodes and edges) to aplurality of interconnected surgical data structures 604, 606, 608. Eachof the surgical data structures 604, 606, 608 may represent a set ofcharacteristics for a surgical procedure (e.g., cataract surgery, heartbypass, sleeve gastrectomy, hip replacement, etc.). Each of the surgicaldata structures 604, 606, 608 are interconnected (e.g., nodes areconnected by edges) such that additional information for portions (e.g.,segments of a video stream) of the surgical procedure can be identified.For example, the first data structure 604 can identify a set ofprocedural stages in the surgical procedure represented by nodes 610,612, 614 and the second data structure 606 can identify a set ofsurgical tools in the surgical procedure represented by nodes 660, 662,664. The first data structure 604 may include one or more nodes 612, 614that are connected by edges 690 to one or more nodes 662, 664 of thesecond data structure 606. The edges 690 connecting the first datastructure 604 to the second data structure 606 can provide additionalrelational metadata about the surgical procedure via the edges 690connecting the nodes of each data structure 604, 606 (for example)including a risk, tools being used or that are about to be used, and/ora warning. Similarly, the second data structure 606 may include node 664that is connected by edge 690 to one or more node 672 of the third datastructure 608. It is also contemplated that any of the nodes 610, 612,614 for the first data structure 604 can be connected by edge 690 to anyof the nodes 670, 672, 674 of the third data structure 608. Each of thesurgical data structures 604, 606, 608 can be connected in multiple waysas long as there are interconnections (e.g., edges) between the surgicaldata structures 604, 606, 608.

The surgical map 600 may process surgical data using metadata (e.g.,procedural state, surgical tools, anatomical features, surgical actions,etc.) associated with nodes or edges from each of the surgical datastructures 604, 606, 608. Additionally, the surgical map 600 maytransition from a first data structure 604 to another surgical datastructure (e.g., the second data structure 606 or the third datastructure 608) in the global surgical graph 602. For example, during asurgical procedure, the global surgical graph 602 can include a firstsurgical data structure 604, a second surgical data structure 606, and athird surgical data structure 608, each associated with a set ofcharacteristics of a surgical procedure. The first surgical datastructure 604 begins at node 610, which includes (for example) adescription of and/or information corresponding to a procedural state.The second surgical data structure 606 begins at node 660, whichincludes (for example) a description of and/or information correspondingto surgical tools (e.g., scalpel, forceps, stapler, etc.) associatedwith a procedural state. The third surgical data structure 608 begins atnode 670, which includes (for example) a description of and/orinformation corresponding anatomical features (e.g., heart, spleen,kidney, etc.) associated with a procedural state. Each of these surgicaldata structures 604, 606, 608 may include nodes that are connected byedges. The edges indicate a transition from one node to the next node(e.g., from a first procedural state to a second procedural state). Thefirst, second, and third surgical data structure 604, 606, 608 mayinclude multiple interconnected nodes that provide inter-relationalinformation between each of the surgical data structures. In someembodiments, the systems and methods described herein may automaticallytransition between the surgical data structures 604, 606, 608 includedin the global surgical graph 602.

In some embodiments, the first surgical data structure 604 may start atnode 610 and represent procedural states of a surgical procedure. Thefirst surgical data structure 604 may include a plurality of nodes 610,612, 614 representing each procedural stage of the surgical procedure.The edges 616, 618, 620 between each of nodes may represent metadata(for example) including medical personnel and/or tools necessary toperform the procedural action. A first procedural state may beassociated with a first node 610, a second procedural state isassociated with a second node 612, and a third procedural state may beassociated with a third node 614. The first edge 616 is associated witha set of procedural metadata and includes a description of and/orinformation corresponding to the first procedural state. For example,the first edge 616 includes metadata associated with a change from afirst procedural state (e.g., node 610) to a second procedural state(e.g., node 612).

The second surgical data structure 606 may start at node 660 and mayrepresent (for example) one or more surgical tools used in the surgicalprocedure. Second surgical data structure 604 may include a plurality ofnodes 660, 662, 664 where each node represents a specific surgical toolof the surgical procedure. In some cases, surgical tools are associatedwith nodes 660, 662, 664 and actions (e.g., movement, appearance,spatial location, etc.) with respect to the surgical tools areassociated with the edges 666, 668, 670. For example, each of the edges666, 668, 670 can represent when a surgical tool is stationary, when asurgical tool is in contact with an anatomical feature, when a surgicaltool is being used by a surgeon, etc. The nodes 660, 662, 664 areconnected by edges 666, 668, 670 which represent surgical interactionsrelated to the associated node-to-node transition.

The third surgical data structure 608 may start at node 670 andrepresent one or more anatomical features (e.g., heart, spleen, kidney,etc.) in the surgical procedure. Third surgical data structure 608 mayinclude a plurality of nodes 670, 672, 674 representing anatomicalfeatures during the surgical procedure. In some aspects, each of thenodes 670, 672, 674 may represent one or more different anatomicalfeatures and may include a sub-tree of nodes associated with eachdifferent anatomical feature. In some cases, anatomical features areassociated with nodes 670, 672, 674 and characteristics (e.g.,incisions, movement, shape, etc.) of the anatomical features areassociated with edges 676, 678, 680. For example, each of the edges 676,678, 680 can represent when an anatomical structure is incised, moved,removed, etc. The nodes 670, 672, 674 are connected by edges 676, 678,680 which can represent surgical interactions with the anatomicalfeatures related to the associated node-to-node transition.

Edges 690 represent nodes from each surgical data structure 604, 606,608 that are interconnected. For example, nodes 612, 614 from the firstdata structure 604 can be connected (e.g., via edges 690) to the nodes662, 664 of the second data structure 606 and, optionally, nodes 672,674 of the third data structure 608 (not shown). Edges 690interconnecting the nodes of each surgical data structure 604, 606, 608may include information included in or identified by metadata. Edges 690between nodes for each surgical data structure 604, 606, 608 may (forexample) identify the procedural state (e.g., by identifying a state ofa patient, progress of a surgery, etc.), identify an action that isbeing performed or is about to be performed (e.g., as identified in anedge that connects a node corresponding to the procedural state with anode corresponding to a next procedural state), and/or identify one ormore considerations (e.g., a risk, tools being used or that are about tobe used, a warning, etc.).

In some embodiments, the surgical data structures can identify asurgical action based on weights assigned to one or more edges betweenthe node-to-node transitions. In some embodiments, a weight may beassigned to each node in addition to, or in lieu of, the weight assignedto each edge. The weights may be associated with a plurality of factorsincluding, (for example) surgical outcomes, risk, prevalence of use,current procedural state, patient characteristics, vital signs,procedure specific parameters, and/or user (e.g., surgeon, nurse or teammember) experience. The risk may be based on maximum risk or a riskbased on the probability of occurrence. The weights may be manuallyassigned when constructing the surgical data structure. In otherembodiments, the surgical weights may be determined or updated using amachine learning algorithm based on collected surgical data, includingsurgical outcomes.

To further illustrate the use of multiple interconnected surgical datastructures, an embodiment describing a subset of steps performed duringthe surgical procedure for sleeve gastrectomy is provided herein. Sleevegastrectomy is a procedure for removing a portion of the stomach topromote weight loss. In this procedure, portions of the stomach areremoved to take on the shape of a tube or “sleeve” which holds lessfood.

FIG. 7 shows a process for using multiple data structures to processsurgical data to produce and/or update an electronic output. In oneembodiment, portions of a video stream 700 from a surgical procedure canbe processed using multiple interconnected data structures. In someembodiments, the multiple interconnected data structures can includemultiple different data structures representing differentcharacteristics of a surgical procedure. For example, the datastructures may correspond to at least one of anatomical features in asurgical procedure, procedural stages of a surgical procedure, and/orsurgical tools in a surgical procedure. Each of these distinct datastructures may include interconnected nodes that provide relationalmetadata that is not available in a single data structure. For example,a data structure corresponding to the procedural stages of a surgicalprocedure may only provide information regarding the detection of asurgical instrument, but does not provide spatial and/or temporalinformation regarding the medical personnel or surgical instrument. Themetadata between interconnected nodes of different data structures mayprovide relational metadata to provide this additional information.

In some embodiments, the surgical data can include video streams from asleeve gastrectomy procedure. FIG. 7 shows portions of a video stream700 of a sleeve gastrectomy procedure processed using the multipleinterconnected data structures. For example, a first data stream 710, asecond data stream 720, a third data stream 730, and a fourth datastream 740 from a sleeve gastrectomy procedure can each be processedusing the multiple interconnected data structures. The first data stream710 can be processed to identify a stage of sleeve gastrectomy, e.g.,surgical workflow detection, according to a first data structure. Thefirst surgical data structure can provide precise and accuratedescriptions of complete surgical procedures for sleeve gastrectomy. Thefirst surgical data structure can describe routes through surgicalprocedures. For example, the first surgical data structure can includemultiple nodes, each node representing a procedural state (e.g.,corresponding to a state of a patient). The surgical data structure canfurther include multiple edges, with each edge connecting multiple nodesand representing a surgical action.

Similarly, the second data stream 720, the third data stream 730, andthe fourth data stream 740 can be processed using other data structuresof the multiple interconnected data structures. For example, the seconddata stream 720 can be processed to identify anatomical featuresdetected during the sleeve gastrectomy procedure using a second datastructure. The second data structure can provide precise and accuratedescriptions of anatomical features in a sleeve gastrectomy procedure.The third data stream 730 can be processed to identify surgicalinstruments detected during the sleeve gastrectomy procedure using athird data structure and the fourth data stream 740 can be processed toidentify surgical events during the sleeve gastrectomy procedure. Eachportion of the video stream 700 can be processed using amulti-dimensional artificial intelligence protocol as described herein.

Each of the data structures may include a plurality of nodes connectedto nodes in another data structure representing additionalcharacteristics of a sleeve gastrectomy procedure. For example, a seconddata structure may represent the anatomical features present in thesleeve gastrectomy procedure and a third data structure may representthe surgical tools used in the sleeve gastrectomy procedure. Theinterconnected nodes in each of these data structures can providerelational metadata regarding the surgical procedure sleeve gastrectomyprocedure. For example, in fourth data stream 740, the relationalmetadata between the multiple data structures can provide relevantinformation that is aggregated over time. The relational metadata canprovide information (for example) including usage of surgical tools nearanatomical features that may cause injury, prolonged usage of surgicalinstruments, medical personnel at specific stages of surgery, oractions/events at specific stages of surgery. This information can becompiled and output as an electronic output (e.g., an operational note).

Specific details are given in the above description to provide athorough understanding of the embodiments. However, it is understoodthat the embodiments can be practiced without these specific details.For example, circuits can be shown in block diagrams in order not toobscure the embodiments in unnecessary detail. In other instances,well-known circuits, processes, algorithms, structures, and techniquescan be shown without unnecessary detail in order to avoid obscuring theembodiments.

Implementation of the techniques, blocks, steps and means describedabove can be done in various ways. For example, these techniques,blocks, steps and means can be implemented in hardware, software, or acombination thereof. For a hardware implementation, the processing unitscan be implemented within one or more application specific integratedcircuits (ASICs), digital signal processors (DSPs), digital signalprocessing devices (DSPDs), programmable logic devices (PLDs), fieldprogrammable gate arrays (FPGAs), processors, controllers,micro-controllers, microprocessors, other electronic units designed toperform the functions described above, and/or a combination thereof.

Also, it is noted that the embodiments can be described as a processwhich is depicted as a flowchart, a flow diagram, a data flow diagram, astructure diagram, or a block diagram. Although a flowchart can describethe operations as a sequential process, many of the operations can beperformed in parallel or concurrently. In addition, the order of theoperations can be re-arranged. A process is terminated when itsoperations are completed, but could have additional steps not includedin the figure. A process can correspond to a method, a function, aprocedure, a subroutine, a subprogram, etc. When a process correspondsto a function, its termination corresponds to a return of the functionto the calling function or the main function.

Furthermore, embodiments can be implemented by hardware, software,scripting languages, firmware, middleware, microcode, hardwaredescription languages, and/or any combination thereof. When implementedin software, firmware, middleware, scripting language, and/or microcode,the program code or code segments to perform the necessary tasks can bestored in a machine readable medium such as a storage medium. A codesegment or machine-executable instruction can represent a procedure, afunction, a subprogram, a program, a routine, a subroutine, a module, asoftware package, a script, a class, or any combination of instructions,data structures, and/or program statements. A code segment can becoupled to another code segment or a hardware circuit by passing and/orreceiving information, data, arguments, parameters, and/or memorycontents. Information, arguments, parameters, data, etc. can be passed,forwarded, or transmitted via any suitable means including memorysharing, message passing, ticket passing, network transmission, etc.

For a firmware and/or software implementation, the methodologies can beimplemented with modules (e.g., procedures, functions, and so on) thatperform the functions described herein. Any machine-readable mediumtangibly embodying instructions can be used in implementing themethodologies described herein. For example, software codes can bestored in a memory. Memory can be implemented within the processor orexternal to the processor. As used herein the term “memory” refers toany type of long term, short term, volatile, nonvolatile, or otherstorage medium and is not to be limited to any particular type of memoryor number of memories, or type of media upon which memory is stored.

Moreover, as disclosed herein, the term “storage medium” can representone or more memories for storing data, including read only memory (ROM),random access memory (RAM), magnetic RAM, core memory, magnetic diskstorage mediums, optical storage mediums, flash memory devices and/orother machine readable mediums for storing information. The term“machine-readable medium” includes, but is not limited to portable orfixed storage devices, optical storage devices, wireless channels,and/or various other storage mediums capable of storing that contain orcarry instruction(s) and/or data.

What is claimed is:
 1. A computer-implemented method comprising:training a multi-dimensional machine learning algorithm based on asurgical training dataset, wherein the surgical training datasetincludes one or more data streams captured during past surgicalprocedures, each of the data streams obtained from a corresponding datacapture device, and wherein the training comprises: creating, using afirst machine learning model, a first surgical data structure thatcorresponds to the surgical training dataset, the first surgical datastructure including a plurality of nodes, each node of the plurality ofnodes representing a procedural state and a set of procedural metadata,each node of the plurality of nodes being connected via at least oneedge to at least one other node of the plurality of nodes; creating,using a second machine learning model, a second surgical data structurethat corresponds to the surgical training dataset, the second surgicaldata structure including a plurality of additional nodes, eachadditional node of the plurality of additional nodes being connected viaat least one edge to at least one other additional node of the pluralityof additional nodes; identifying, by the multi-dimensional machinelearning algorithm, an interconnection between the first surgical datastructure and the second surgical data structure based on one or moresurgical events corresponding to the procedural state associated with afirst node from the first surgical data structure, the surgical eventsdetermined from the one or more data streams, and in response creatingone or more edges, each edge of the one or more edges connecting thefirst node of the first surgical data structure to a second node of thesecond surgical data structure, each edge of the one or more edges beingassociated with additional metadata corresponding to the one or moresurgical events; using the multi-dimensional machine learning algorithmthat has been trained to analyze a surgical procedure based on asurgical dataset associated with said surgical procedure, wherein theanalyzing comprises: associating, the first node from the first surgicaldata structure, the second node from the second surgical data structure,and the one or more edges between the first node and the second node,using the surgical dataset of said surgical procedure; generatingelectronic data to be associated with a portion of the surgical dataset,the electronic data describing an operational note based at least on theprocedural state associated with the first node, a data associated withthe second node, and the additional metadata associated with an edgebetween the first node and the second node; and outputting theelectronic data, the output associating the electronic data with theportion of the surgical dataset.
 2. The method of claim 1, wherein eachadditional node of the plurality of nodes further representing at leastone of an anatomical structure, a phoneme, an identity of medicalpersonnel, a surgical tool, or surgical actions, and a set of associatedmetadata.
 3. The method of claim 1, further comprising creating one ormore additional surgical data structures that correspond to the surgicaldataset, the one or more additional surgical data structures includinganother plurality of additional nodes, each additional node of theanother plurality of additional nodes being connected via at least oneedge to at least one other additional node of the another plurality ofadditional nodes, at least one node of the another plurality ofadditional nodes being connected via at least one edge to at least oneother node of the first data structure and/or the second surgical datastructure.
 4. The method of claim 3, wherein each additional node of theanother plurality of nodes representing a surgical characteristic and aset of surgical characteristic metadata.
 5. The method of claim 1,wherein the one or more data streams comprise: multiple different videostreams; multiple different audio streams; and/or multiple differentsignals from electronic devices.
 6. The method of claim 1, wherein eachof the one or more data streams having been generated at and receivedfrom an electronic device configured and positioned to capture dataduring the surgical procedure, and the one or more data streamsincluding a set of information units, each information unit of the setof information units corresponding to a different temporal associationrelative to other information units of the set of information units;processing the one or more data streams using a multi-dimensionalartificial-intelligence protocol based on the first data structure andthe second surgical data structure, the processing including repeatedlydetecting new information units of the set of information units receivedas part of the one or more data streams and processing each data portionof the new information unit by: extracting features of the informationunit to identify one or more features, wherein each of the one or morefeatures corresponds to an incomplete subset of the data portion;classifying and/or localizing, for each of the one or more features andduring a stage of the multi-dimensional artificial-intelligenceprotocol, the feature as corresponding to a particular object type of aset of predefined object types; determining, during the stage of themulti-dimensional artificial-intelligence protocol, a particular stateof the set of procedural states based on the classifying and/orlocalizing and at least some of the metadata in the first datastructure; identifying one or more properties corresponding to the oneor more features; comparing the one or more properties to the metadataidentified for the procedural state in the first data structure and themetadata identified for the second surgical data structure; determining,based on the comparison and one or more predefined conditions and duringanother stage of the multi-dimensional artificial-intelligence protocol,whether a procedural departure occurred; and updating a filecorresponding to a particular procedural instance to include a fileupdate corresponding to the information unit, wherein the file updateidentifies the particular state, and the file update includes arepresentation of a result of at least part of the comparison when it isdetermined that a procedural departure occurred; and outputting theupdated file.
 7. The method of claim 6, wherein classifying and/orlocalizing each of the one more features includes: dividing a portion ofthe one or more features, the portions of one or more features beingassociated with different timestamps or time windows relative to eachother; and determining, for each of the one or more features, metadatacorresponding to the feature based on the first data structure and thesecond surgical data structure, wherein temporal information isidentified based on a time stamp or time window associated with eachportion of the one or more features determined to be corresponding tothe metadata.
 8. The method of claim 6, wherein the one or more datastreams from the surgical procedure includes a set of video streams,each video stream correspond to frames within a video stream, whereinprocessing each data stream of the one or more data streams furthercomprises: assigning, based on the object type in each information unit,each video stream of the set of video streams to a confidential-videoclassification or a non-confidential-video classification; andaugmenting each video stream of the set of video streams based on theconfidential-video classification to produce an augmented video stream;and storing the augmented video stream.
 9. A computer-program producttangibly embodied in a non-transitory machine-readable storage medium,including instructions configured to cause one or more data processorsto perform actions including: training a multi-dimensional machinelearning algorithm based on a surgical training dataset, wherein thesurgical training dataset includes one or more data streams capturedduring past surgical procedures, each of the data streams obtained froma corresponding data capture device, and wherein the training comprises:creating, using a first machine learning model, a first surgical datastructure that corresponds to the surgical training dataset, the firstsurgical data structure including a plurality of nodes, each node of theplurality of nodes representing a procedural state and a set ofprocedural metadata, each node of the plurality of nodes being connectedvia at least one edge to at least one other node of the plurality ofnodes; creating, using a second machine learning model, a secondsurgical data structure that corresponds to the surgical trainingdataset, the second surgical data structure including a plurality ofadditional nodes, each additional node of the plurality of additionalnodes being connected via at least one edge to at least one otheradditional node of the plurality of additional nodes; identifying, bythe multi-dimensional machine learning algorithm, an interconnectionbetween the first surgical data structure and the second surgical datastructure based on one or more surgical events corresponding to theprocedural state associated with a first node from the first surgicaldata structure, the surgical events determined from the one or more datastreams, and in response creating one or more edges, each edge of theone or more edges connecting the first node of the first surgical datastructure to a second node of the second surgical data structure, eachedge of the one or more edges being associated with additional metadatacorresponding to the one or more surgical events; using themulti-dimensional machine learning algorithm that has been trained toanalyze a surgical procedure based on a surgical dataset associated withsaid surgical procedure, wherein the analyzing comprises: associating,the first node from the first surgical data structure, the second nodefrom the second surgical data structure, and the one or more edgesbetween the first node and the second node, using the surgical datasetof said surgical procedure; generating electronic data to be associatedwith a portion of the surgical dataset, the electronic data describingan operational note based at least on the procedural state associatedwith the first node, a data associated with the second node, and theadditional metadata associated with an edge between the first node andthe second node; and outputting the electronic data, the outputassociating the electronic data with the portion of the surgicaldataset.
 10. The computer-program product of claim 9, wherein eachadditional node of the plurality of nodes further representing at leastone of an anatomical structure, a phoneme, an identity of medicalpersonnel, a surgical tool, or surgical actions, and a set of associatedmetadata.
 11. The computer-program product of claim 9, furthercomprising creating one or more additional surgical data structures thatcorrespond to the surgical dataset, the one or more additional surgicaldata structures including another plurality of additional nodes, eachadditional node of the another plurality of additional nodes beingconnected via at least one edge to at least one other additional node ofthe another plurality of additional nodes, at least one node of theanother plurality of additional nodes being connected via at least oneedge to at least one other node of the first data structure and/or thesecond surgical data structure.
 12. The computer-program product ofclaim 11, wherein each additional node of the another plurality of nodesrepresenting a surgical characteristic and a set of surgicalcharacteristic metadata.
 13. The computer-program product of claim 9,wherein the one or more data streams comprise: multiple different videostreams; multiple different audio streams; and/or multiple differentsignals from electronic devices.
 14. The computer-program product ofclaim 13, wherein each of the one or more data streams having beengenerated at and received from an electronic device configured andpositioned to capture data during the surgical procedure, and the one ormore data streams including a set of information units, each informationunit of the set of information units corresponding to a differenttemporal association relative to other information units of the set ofinformation units; processing the one or more data streams using amulti-dimensional artificial-intelligence protocol based on the firstdata structure and the second surgical data structure, the processingincluding repeatedly detecting new information units of the set ofinformation units received as part of the one or more data streams andprocessing each data portion of the new information unit by: extractingfeatures of the information unit to identify one or more features,wherein each of the one or more features corresponds to an incompletesubset of the data portion; classifying and/or localizing, for each ofthe one or more features and during a stage of the multi-dimensionalartificial-intelligence protocol, the feature as corresponding to aparticular object type of a set of predefined object types; determining,during the stage of the multi-dimensional artificial-intelligenceprotocol, a particular state of the set of procedural states based onthe classifying and/or localizing and at least some of the metadata inthe first data structure; identifying one or more propertiescorresponding to the one or more features; comparing the one or moreproperties to the metadata identified for the procedural state in thefirst data structure and the metadata identified for the second surgicaldata structure; determining, based on the comparison and one or morepredefined conditions and during another stage of the multi-dimensionalartificial-intelligence protocol, whether a procedural departureoccurred; and updating a file corresponding to a particular proceduralinstance to include a file update corresponding to the information unit,wherein the file update identifies the particular state, and the fileupdate includes a representation of a result of at least part of thecomparison when it is determined that a procedural departure occurred;and outputting the updated file.
 15. The computer-program product ofclaim 13, wherein classifying and/or localizing each of the one morefeatures includes: dividing a portion of the one or more features, theportions of one or more features being associated with differenttimestamps or time windows relative to each other; and determining, foreach of the one or more features, metadata corresponding to the featurebased on the first data structure and the second surgical datastructure, wherein temporal information is identified based on a timestamp or time window associated with each portion of the one or morefeatures determined to be corresponding to the metadata.
 16. Thecomputer-program product of claim 13, wherein the one or more datastreams from the surgical procedure includes a set of video streams,each video stream correspond to frames within a video stream, whereinprocessing each data stream of the one or more data streams furthercomprises: assigning, based on the object type in each information unit,each video stream of the set of video streams to a confidential-videoclassification or a non-confidential-video classification; andaugmenting each video stream of the set of video streams based on theconfidential-video classification to produce an augmented video stream;and storing the augmented video stream.
 17. A system comprising: one ormore data processors; and a non-transitory computer readable storagemedium containing instructions which when executed on the one or moredata processors, cause the one or more data processors to performactions including: training a multi-dimensional machine learningalgorithm based on a surgical training dataset, wherein the surgicaltraining dataset includes one or more data streams captured during pastsurgical procedures, each of the data streams obtained from acorresponding data capture device, and wherein the training comprises:creating, using a first machine learning model, a first surgical datastructure that corresponds to the surgical training dataset, the firstsurgical data structure including a plurality of nodes, each node of theplurality of nodes representing a procedural state and a set ofprocedural metadata, each node of the plurality of nodes being connectedvia at least one edge to at least one other node of the plurality ofnodes; creating, using a second machine learning model, a secondsurgical data structure that corresponds to the surgical trainingdataset, the second surgical data structure including a plurality ofadditional nodes, each additional node of the plurality of additionalnodes being connected via at least one edge to at least one otheradditional node of the plurality of additional nodes; identifying, bythe multi-dimensional machine learning algorithm, an interconnectionbetween the first surgical data structure and the second surgical datastructure based on one or more surgical events corresponding to theprocedural state associated with a first node from the first surgicaldata structure, the surgical events determined from the one or more datastreams, and in response creating one or more edges, each edge of theone or more edges connecting the first node of the first surgical datastructure to a second node of the second surgical data structure, eachedge of the one or more edges being associated with additional metadatacorresponding to the one or more surgical events; using themulti-dimensional machine learning algorithm that has been trained toanalyze a surgical procedure based on a surgical dataset associated withsaid surgical procedure, wherein the analyzing comprises: associating,the first node from the first surgical data structure, the second nodefrom the second surgical data structure, and the one or more edgesbetween the first node and the second node, using the surgical datasetof said surgical procedure; generating electronic data to be associatedwith a portion of the surgical dataset, the electronic data describingan operational note based at least on the procedural state associatedwith the first node, a data associated with the second node, and theadditional metadata associated with an edge between the first node andthe second node; and outputting the electronic data, the outputassociating the electronic data with the portion of the surgicaldataset.
 18. The system of claim 17, wherein each additional node of theplurality of nodes further representing at least one of an anatomicalstructure, a phoneme, an identity of medical personnel, a surgical tool,or surgical actions, and a set of associated metadata.
 19. The system ofclaim 17, further comprising creating one or more additional surgicaldata structures that correspond to the surgical dataset, the one or moreadditional surgical data structures including another plurality ofadditional nodes, each additional node of the another plurality ofadditional nodes being connected via at least one edge to at least oneother additional node of the another plurality of additional nodes, atleast one node of the another plurality of additional nodes beingconnected via at least one edge to at least one other node of the firstdata structure and/or the second surgical data structure.
 20. The systemof claim 19, wherein each additional node of the another plurality ofnodes representing a surgical characteristic and a set of surgicalcharacteristic metadata.